The Uni-Mol2 with 1.1B parameters also outperforms

Figure 1 shows the change of accuracy under different cutoff value. However, for gender classification under CelebA dataset, thetrade-offbetweenλval and accuracyisnotveryclear;and wesuspect that under suchscenario, focusing on hard samples does not harm the performance of easy samples, and thus benefits the classifier. Figure 1 shows the change of fairness (equalized odds) under different cutoff value. Suppose we have a large unlabeled training set of sizeN and a small labeled validation set { xvalj,yvalj,1 j M} with M N. In each training step, we sample a small mini-batch of size n(n < N) from training set and perform random augmentation twice to obtain a subset { xi,1 i 2n} and we update the contrastive encoderf with parameterθ. During validation, we freeze the contrastive encoder and train a downstream linear classifierg with parameterω for classification task.